The present invention relates to a data optimization engine for optimizing data transmission bandwidth and storage capacity in electronic systems and computer networks. More particularly, the present invention relates to highly modular data optimization engines, which are designed to be reconfigurable in an efficient and simplified manner to work with different protocols, and methods therefor.
Data transmission links and data storage devices are basic building blocks of modern electronic systems and computer networks. Data transmission links are present in every electronic system and are also fundamental for interconnecting nodes in a computer network. In an electronic system, such as in a computer for example, a data transmission link such as an address bus or a data bus may be employed to transmit digital data between two or more subsystems. Within a computer network (e.g., a local area network, a metro area network, a wide area network, or the Internet), data may be transmitted from one networked device to another via one or more data transmission links using a variety of well-known networking protocols. As is well known, the data transmission links themselves may be implemented using any physical media, such as wireless, copper or fiber optics, and may transfer data in a serial or parallel format.
In modern high-speed electronic systems, the data transmission link has long been regarded as one of the bottlenecks that limit overall system performance. To facilitate discussion of the foregoing, FIG. 1 shows simplified CPU, bus, and memory subsystems within an exemplary computer 100. In a typical computer system, such as in computer 100, a central processing unit (CPU) 102 typically operates at a much higher speed than the speed of a bus 104, which is employed to transmit data between CPU 102 and the various subsystems (such as a memory subsystem 106). By way of example, in some Windows™-based or Unix-based computer systems, it is not unusual to see a CPU having a clock speed in the Gigahertz range being coupled to a data bus running in the low hundreds of Megahertz range. There are many reasons behind the disparity between the CPU speed and the bus clock speed. For one, advances in processor technologies tend to follow the so-called Moore's law, which states that the speed of a typical electronic device can be expected to double roughly every 18 months. The clock speed of a typical data or address bus, on the other hand, is limited by the impedance and other physical characteristics of conductive traces that comprise the bus. Thus, it is often times impractical to run these buses at a higher speed to match the speed of the fast CPU due to issues related to power, interference, and the like.
The data storage device, such as a memory subsystem 106 within computer system 100, also represents another bottleneck to higher overall system performance. With regard to memory subsystem 106, there are generally three issues: 1) the speed of data transfer to and from memory subsystem 106, 2) the operating speed of memory subsystem 106, and 3) the storage capacity of memory subsystem 106. With regard to the data transfer speed issue, the discussion above regarding the data transmission link bottleneck applies. With regard to the operating speed of memory subsystem 106, dynamic random access memory (DRAM), which is widely employed for storage of data and instructions during operation, must be refreshed periodically (by a memory controller 108 as shown or by some type of refresh circuitry), and the capacitors employed in the DRAM to store the charges representing the 0's and 1's have a finite response time. Together, these factors tend to limit the speed of a typical DRAM to well below the operating speed of the CPU. Even if static random access memory (SRAM) is employed (assuming the high power consumption and low density issues can be tolerated) in memory subsystem 106, the operating speed of a typical SRAM is also well below that of a typical CPU in computer system 100.
Because of the relative slow response of memory subsystem 106, attempts have been made, some more successful than others, to improve memory access speed. Caching is one popular technique to improve the memory access speed for frequently used or most recently used data. In caching, a small amount of dedicated very high-speed memory 110 is interposed between memory subsystem 106 and CPU 102. This high-speed memory is then employed to temporarily store frequently accessed or most recently used data. When there is a memory read request from the CPU, the cache memory is first checked to see whether it can supply the requested data. If there is a cache hit (i.e., the requested data is found in the cache memory), the faster cache memory, instead of the slower main memory, supplies the requested data at the higher cache memory access speed.
Caching, however, increases the overall complexity of the computer system architecture and its operating system. Further, the use of expensive and power-hungry cache memory (e.g., on-board high speed custom SRAM) disadvantageously increases cost, power consumption, and the like. Furthermore, the cache hit rate is somewhat dependent on the software application and other parameters. If the cache hit rate is low, there may not be a significant improvement in memory access speed to justify the added complexity and cost of a cache subsystem.
As mentioned above, the memory capacity in memory subsystem 106 also represents another constraint to higher overall system performance. Modern complex software, which is often employed to manipulate large database, graphics, sound, or video files, requires a large amount of main memory space for optimum performance. The performance of many computer systems can be greatly improved if more storage is provided in the computer system's main memory. Due to power consumption, board space usage, and cost concerns, however, most computer systems are however manufactured and sold today with a less-than-optimum amount of physical memory on board. Consequently, the overall system performance suffers.
The same three issues pertaining to main memory 106 (i.e., the speed of data transfer to and from memory, the operating speed of the memory, and the storage capacity) also apply to a permanent memory subsystem (such as a hard disk). When a hard disk drive is employed for storing data, for example, the limited speed of the data transmission link between the hard disk drive and the main system bus, the slow access time due to the mechanical rotation nature of the hard disk's platters and the mechanical movement of the actuator arm that contains the read/write head, as well as the fixed storage capacity of the platters all represent factors that tend to limit system performance. Yet, with the advent of the Internet and improved multimedia technologies, users nowadays routinely transmit and store large graphics, video, and sound files using the permanent memory subsystem in their computers. Consequently, it is generally desirable to increase both the memory access speed and the storage capacity of the permanent memory subsystem.
The same three issues pertaining to main memory 106 (i.e., the speed of data transfer to and from memory, the operating speed of the memory, and the storage capacity) also apply to Network-Assisted Storage (NAS) systems, storage area networks (SANs), RAID storage systems, and other networked electromagnetic or optical-based data storage systems. With reference to FIG. 2, irrespective of the protocol implemented on a transmission link 202 between a drive controller 204 and the actual storage media 206 (e.g., hard disks, optical platters, and the like), storage performance can be improved if the effective data throughput through transmission link 202 can be improved. This is true irrespective whether the protocol implemented is serial ATA (S-ATA), IDE, FCAL, SCSI, Fiber Channel over Ethernet, SCSI over Ethernet, or any other protocol employed to transfer data between disk controller 204 and storage media 206. With respect to the storage capacity issue, there is a fixed capacity to storage media 206 based on physical limitations and/or formatting limitations. From a cost-effectiveness standpoint, it would be desirable to transparently increase the capacity of storage media 306 without requiring a greater number and/or larger platters, or changing to some exotic storage media.
The data transmission bandwidth bottleneck also exists within modern high-speed computer networks, which are widely employed for carrying data among networked devices, whether across a room or across a continent. In a modern high-speed computer network, the bottlenecks may, for example, reside with the transmission media (e.g., the wireless medium, the copper wire, or the optical fiber) due to the physical characteristics of the media and the transmission technology employed. Further, the bottleneck may also reside with the network switches, hubs, routers, and/or add-drop multiplexers which relay data from one network node to another. In these devices, the line cards and/or switch fabric are configured to operate at a fixed speed, which is typically limited by the speed of the constituent devices comprising the line card. The device speed is in turn dictated the latest advances in microelectronics and/or laser manufacturing capabilities. In some cases, the bottleneck may be with the protocol employed to transmit the data among the various networked devices. Accordingly, even if the transmission media itself (such as a fiber optic) may theoretically be capable of carrying a greater amount of data, the hardware, software, and transmission protocols may impose a hard limit on the amount of data carried between two nodes in a computer network.
To further discuss the foregoing, there are shown in FIG. 3, in a simplified format, various subsystems of a typical Ethernet-based network 300. Components of Ethernet-based network 300 are well known and readily recognized by those skilled in the art. In general, digital data from a Media Access Controller (MAC) 302 is transformed into physical electrical or optical signals by a transceiver 304 to be transmitted out onto a Ethernet network 308 via a data transmission link 306, which is an Ethernet link in this case. MAC 302, as well as transceiver 304, generally operate at a predefined speed, which is dictated in part by the Ethernet protocol involved (e.g., 10 Mbps, 100 Mbps, 1 Gbps, or 10 Gbps). Thus, the throughput of data through the Ethernet arrangement 300 of FIG. 3 tends to have a finite limit, which cannot be exceeded irrespective of capacity requirement or the theoretical maximum capacity of data transmission link 306.
As the network grows and the capacity requirement for Ethernet-based network 300 increases, it is customary to upgrade MAC 302 and transceiver 304 and other associated electronics to enable data transmission link 306 to carry more data. With the advent of the Internet, however, a 300% growth in data traffic per year is not unusual for many networks. A hardware upgrade to one of the higher speed protocols, unfortunately, tends to involve network-wide disruptive changes (since the sending and receiving network nodes must be upgraded to operate at the same speed). A system-wide upgrade is also costly as many network nodes and components must be upgraded simultaneously to physically handle the higher speed protocol. It would be desirable to have the ability to enable Ethernet 300 to effectively carry more data for a given transmission speed. It would also be desirable to have the ability to upgrade, in a scalable manner, selective portions of the network so that both the upgraded and the legacy equipment can interoperate in an automatic, transparent manner.
In a commonly-owned, co-pending patent application entitled Data Optimization Engines And Methods Therefor (filed by inventor Isaac Achler on the same date, and incorporated by reference herein), various implementations of a data optimization engine and methods therefor are described in detail. In particular, various implementations of an optimization processor which are capable of performing at least one or both of the compression/decompression and encryption/decryption tasks are described in detail. Since the optimization processor and data optimization engine described in the above-discussed patent application have utility in many different environments, such as in computer systems and computer networks to transparently optimize the data transmission bandwidth, in storage systems (e.g., hard disks, RAID systems, Network Assistant Storage or NAS systems, Storage Area Networks or SANs, and other networked electromagnetic or optical-based data storage systems) to optimize the data transmission bandwidth and storage capacity, it is realized that it would be highly advantageous to create a universal, modular data optimization engine that can be easily and efficiently adapted to work with different protocols.
Generically speaking, for a data optimization engine to optimize a stream of data having a given protocol, certain issues need to be addressed in addition to the actual compression/decompression and/or encryption/decryption tasks themselves. To allow the data optimization engine to be universal, protocol adaptation, i.e., the translation of the data from the protocol received to one that can be understood by the optimization processor, needs to be performed. After the data is optimized by the optimization processor, the optimized data needs to undergo protocol adaptation again prior to outputting.
Data alignment and data parsing are also protocol-specific tasks that need to be handled differently for different data input protocols. Data alignment refers to the need to recognize and frame the incoming data properly with respect to some reference data frame as the incoming data is received. Data alignment facilitates data parsing, since efficient data parsing relies on the correct relative positioning of the various data fields within some reference data frame. For each data frame that can be optimized (since not all data frames are eligible for optimization), some portion of the optimizable data frame needs to be preserved while other portions can be optimized by the optimization processor. Data parsing separates the optimizable portion from the non-optimizable portion of the data frame so that the optimizable portion can be optimized by the optimization processor.
A related task is optimizable data handling, which refers to the need to reassemble the data frame, putting together the non-optimizable portion of the data frame with the optimizable portion after the optimization processor has finished its optimizing task. Optimizable data handling ensures that a properly reassembled data frame is presented at the output for transmission to the next hop or to the final destination. As mentioned, some incoming data frames may be non-optimizable, e.g., due to an explicit request from software or from some other higher layer in the communication stack. Bypass data handling needs to be performed on the incoming data to ensure that the data optimization engine will handle these non-optimizable data frames properly.
Another task is congestion control, which is necessary to ensure that the optimization processor is not overloaded if incoming data is received at the data optimization engine in rapid bursts. Congestion control gives the optimization processor time to complete its optimization task on a frame-by-frame basis while minimizing and/or eliminating the possibility of dropping incoming data frames if they arrive in rapid bursts. Yet another related task is traffic handling, which ensures that while data optimization takes place within the inline data optimization engine, the communication channel remains error-free. Traffic handling is necessary if the data optimization engine is to be transparent to the transmitting and receiving devices.
Since these tasks all need to be performed, and they are all different for different protocols, the challenge of creating a universal data optimization engine rests, in part, in the ability to innovatively section and modularize the data optimization engine and to innovatively arrange the various circuits therein in a manner such that when the data optimization engine needs to be reconfigured to work with a different protocol, the reconfiguration may be done quickly and efficiently and changes to the data optimization engine may be minimized.
In view of the foregoing, there are desired improved techniques and apparatus for optimizing the data transmission bandwidth in data buses and network transmission links, as well as for optimizing the storage capacity of temporary and permanent memory in electronic devices and computer networks.